Cerebral Cortex April 2010;20:912--928

doi:10.1093/cercor/bhp153

Advance Access publication August 13, 2009

Tracking Lexical Access in SpeechProduction: ElectrophysiologicalCorrelates of Word Frequency andCognate Effects

Kristof Strijkers1,2, Albert Costa2 and Guillaume Thierry3

1Universitat de Barcelona, Department of Psicologia Basica,

GRNC, 08035 Barcelona, Cataluna, Spain, 2Universitat Pompeu

Fabra, Dept. de Tecnologia, ICREA, 08018 Barcelona, Cataluna,

Spain and 3Bangor University, School of Psychology, ESRC

Centre for Research on Bilingualism in Theory and Practice,

Bangor, LL57 2DG Wales, UK

The present study establishes an electrophysiological index oflexical access in speech production by exploring the locus of thefrequency and cognate effects during overt naming. We conducted2 event-related potential (ERP) studies with 16 Spanish--Catalanbilinguals performing a picture naming task in Spanish (L1) and 16Catalan--Spanish bilinguals performing a picture naming task inSpanish (L2). Behavioral results showed a clear frequency effectand an interaction between frequency and cognate status. The ERPelicited during the production of high-frequency words divergedfrom the low-frequency ERP between 150 and 200 ms post-targetpresentation and kept diverging until voice onset. The same resultswere obtained when comparing cognate and noncognate con-ditions. Positive correlations were observed between naminglatencies and mean amplitude of the P2 component following thedivergence, for both the lexical frequency and the cognate effects.We conclude that lexical access during picture naming beginsapproximately 180 ms after picture presentation. Furthermore,these results offer direct electrophysiological evidence for an earlyinfluence of frequency and cognate status in speech production.The theoretical implications of these findings for models of speechproduction are discussed.

Keywords: ERP, language production, lexical activation, time course

Introduction

The ease with which we produce speech may lead us to think

that the cognitive and brain mechanisms put at play in this skill

are rather simple. However, speech production is a complex

process which entails the orchestration of many processes that

unfold over time (e.g., Dell 1986; Caramazza 1997; Levelt et al.

1999). In recent years, the amount of psycholinguistic

experimental research exploring these processes has in-

creased, leading to more detailed models of speech production.

However, the same cannot be said regarding the investigation

of the time course of the neural events underpinning these

processes. The present article aims at helping to fill this gap by

exploring the electrophysiological correlates of 2 robust

psycholinguistic phenomena in picture naming; the frequency

and the cognate effect.

Cognitive models of single word production assume that the

translation of our communicative goal into speech occurs at

various levels of representation. Speaking probably involves at

least 1) the retrieval of conceptual information, 2) the selection

of the words corresponding to the intended message, 3) the

phonological encoding of the selected words, and 4) the

retrieval of the articulatory plans (e.g., Dell 1986; Caramazza

1997; Levelt et al. 1999). Although there is still a debate

regarding how these processes unfold over time, researchers

generally agree on the existence of some sequentiality. That is,

concepts are retrieved before words are selected and these in

turn are selected before their corresponding phonemes are

retrieved. Given this general functional architecture, it is

relevant to describe not only the neural structures implicated

in the representation of different types of information but also

the time course of their involvement.

The time course of lexical access in speech production has

been studied using a variety of chronometric tasks (e.g.,

Schriefers et al. 1990; Dell and O’Seaghdha 1991; Wheeldon

and Levelt 1995). These studies have provided evidence for the

hypothesis of sequentiality in speech production by showing

that a word’s conceptual/semantic and syntactic properties are

retrieved before its phonological form becomes available.

Recently, event-related potential (ERP) studies have supported

such a sequence of processing in speech production (e.g., Van

Turennout et al. 1997, 1998; Schmitt et al. 2000; Jescheniak et al.

2002; Rodriguez-Fornells et al. 2002; Schiller et al. 2003a). These

studies used the Lateralized Readiness Potential (LRP; e.g., Coles,

1989; Miller et al. 1992) and the N200 (a component supposed

to reflect response inhibition; e.g., Pfefferbaum et al. 1985; Kok

1986; Eimer 1993) in linguistic go/no-go tasks in order to obtain

precise measurements of the temporal distance between

different stages of speech production. LRP and N200 data

indicate that conceptual activation unfolds during the first 150

ms of processing (e.g., Thorpe et al. 1996; see also Johnson and

Olshausen 2005; Hauk et al. 2007), lexico-semantic information

is processed 90--120 ms before phonological information (e.g.,

Van Turennout et al. 1997; Schmitt et al. 2000), and syntactic

information is retrieved approximately 40 ms before phonolog-

ical information (e.g., Van Turennout et al. 1998).

In an interesting meta-analysis, Indefrey and Levelt (2004)

integrated results of such chronometric ERP studies with the

time course of neural activation revealed by magnetoencepha-

lography (MEG) in overt picture naming tasks (Salmelin et al.

1994; Levelt et al. 1998; Maess et al. 2002). According to this

meta-analysis and relative to picture onset (time 0), 1) lexical

access (understood as lemma retrieval) is estimated to start as

early as 150 ms and reach completion at around 275 ms, 2)

phonological encoding is thought to take place between 275

and 400 ms; and 3) syllabification is estimated to unfold

between 400 and 600 ms.

However, the chronometric interpretation proposed by

Indefrey and Levelt’s (2004) meta-analysis may be affected by

the fact that the tasks that participants performed in the ERP

studies cited above differed markedly from those used in the

MEG studies considered for comparison. In order to avoid

effects of speech-related motor artifacts (e.g., Wohlert 1993;

Masaki et al. 2001) none of the ERP experiments actually

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involved speech production, but rather consisted of button

press responses. Furthermore, the tasks were complex and

difficult (go/no-go decision, combined with left/right button

press decision, followed by production of a pronoun sentence

comprising the stimuli), and implied meta-linguistic judgments

by the participants (e.g., ‘‘if the word refers to an animal, then

press the right button if the animal name starts with the letter

b or press the left button if it starts with the letter s’’). It cannot

be excluded that such conditions may have triggered additional

cognitive processes affecting the timing of the key processes

involved in natural speech production. Also, the time course

proposed by models of picture naming is at odds with some

time course estimates derived from picture processing and

word recognition studies. Whereas some authors argue that

during picture processing the brain engages in semantic

analysis already before 150 ms after stimulus onset (e.g.,

Thorpe et al. 1996; Johnson and Olshausen 2005; Hauk et al.

2007), others dispute the existence of such early differences

(e.g., Holcomb and McPherson 1994; Kiefer 2001; Eddy et al.

2006; Sitnikova et al. 2006). Similarly, some ERP studies of visual

word recognition converge in showing that lexical processing

starts within 200 ms of picture onset (e.g., Sereno et al. 1998;

Hauk and Pulvermuller 2004; Hauk et al. 2006, 2009) but other

studies using the masked priming paradigm showed that it

takes at least 250--300 ms to start retrieving lexical information

(e.g., Holcomb and Grainger 2006; Grainger and Holcomb

2009). Here we sought to obtain new evidence regarding the

time course of lexical access in natural speech production by

asking high proficient bilinguals to simply name pictures

overtly while recording ERPs.

As hinted above, recording electroencephalography (EEG)

signals during overt speech is methodologically disputable, due

to articulation-related artefacts. Indeed, activation of the mouth

and face musculature produces electrical potentials larger than

brain-generated signals by a factor of 10--100. These large

motor and motor preparation potentials, well beyond the EEG

amplitudes collected in silent response tasks such as button-

presses, could potentially overshadow the cognitive brain

activity at interest. However, one ERP study, directly comparing

overt versus covert speech (Eulitz et al. 2000), and several MEG

studies of overt picture naming (e.g., Levelt et al. 1998; Salmelin

et al. 1994; Maess et al. 2002) have shown that reliable

measures of brain activity can be taken at least until 400 ms

after picture onset. As far as we are interested in early phases of

picture naming, this simple paradigm should offer insight into

the time course of lexical retrieval.

In order to trigger ERP differences during lexical access in

speech production, we chose to manipulate word frequency as

an independent variable. There is ample evidence that word

frequency affects the speed and accuracy with which picture

naming is performed: pictures with high-frequency names tend

to be named faster and more accurately by normal and brain-

damaged speakers than pictures with low-frequency names (e.g.,

Oldfield and Wingfield 1968; Dell 1990; Jescheniak and Levelt

1994; Navarrete et al. 2006; Almeida et al. 2007; Kittridge et al.

2007). These results suggest that lexical frequency influences or

even determines the speed of lexical access (for a similar

approach in language comprehension see e.g., King and Kutas

1998; Sereno et al. 1998; Hauk and Pulvermuller 2004).

However, 2 issues must be kept in mind: 1) lexical frequency

correlates with conceptual variables such as imageability and

familiarity and 2) the stage at which word frequency exerts its

effects remains debated. These 2 considerations pose difficulties

for the interpretation of ERP differences driven by lexical

frequency.

One way to circumvent issue (1) is to manipulate another

independent variable that exerts a reliable and consistent effect

in picture naming but that is not confounded by conceptual

properties. In the present study we tested bilingual individuals

and we manipulated not only lexical frequency but also the

cognate status of target picture names. The cognate effect

refers to the observation of faster naming latencies in bilingual

speakers for pictures whose translations are phonologically

similar across languages (e.g., the Spanish--English pair ‘‘gui-

tarra’’— guitar) as compared with pictures with phonologically

dissimilar translations (e.g., the Spanish--English pair ‘‘perro’’—

dog; e.g., Costa et al. 2000, 2005; Christoffels et al. 2007).

Critically, the cognate status of a word depends on its

phonology (formal similarity across translations) and is

therefore unrelated to conceptual variables of the sort de-

scribed above (see description of stimuli and appendix B).

(There has been one proposal arguing that cognate status

might influence conceptual processing; Van Hell and De Groot

1998. Cognates should have larger conceptual overlap com-

pared with noncognates. However, such an account has

difficulties in explaining the performance of brain-damaged

speakers or certain tip-of-the-tongue states and seems to have

problems with theoretical logic [for a clear overview see Costa

et al. 2005]. Furthermore, results from Van Hell and De Groot’s

(1998) study are not that clear for concrete cognates [the type

of stimuli used here] and are based on the sole assumption that

word association does not involve lexical processes. Finally, the

absence of any significant differences between cognate and

noncognate words for the conceptual ratings of the stimuli

used in the present study is at odds [at least for concrete

cognates in high proficient bilinguals] with a conceptual

account.). In a recent ERP study of overt naming involving

cognates, Christoffels et al. (2007) found a significant negative

enhancement around 300 ms after stimulus presentation for

cognate ERPs compared with noncognate ERPs, which was

interpreted in support of the phonological origin of the

cognate effect.

Issue (2), that is, the locus of the frequency effect in speech

production, is more complex to address. According to some

researchers, word frequency affects the retrieval of lexical

nodes from the lexicon (e.g., Caramazza et al. 2001; Navarrete

et al. 2006; Almeida et al. 2007), whereas others argue that

frequency only affects the retrieval of phonological information

during production (e.g., Jescheniak and Levelt 1994; Jescheniak

et al. 2003). Our study can only be informative regarding the

time course of lexical access if indeed word frequency

influences the retrieval of lexical representations and not just

the retrieval of phonological segments. However, we believe

this issue to be an empirical one. Importantly, recent

behavioral, neuroimage and patient studies all provide clear

evidence supporting the notion that frequency affects both

lexical and phonological processing in speech production (e.g.,

Navarrete et al. 2006; Graves et al. 2007; Kittridge et al. 2007;

Knobel et al. 2008). Thus, according to this new evidence, it is

reasonable to expect early effects for frequency (150--200 ms,

e.g., Indefrey and Levelt 2004).

To summarize, our main goal was to index the onset of

lexical access in speech production by comparing ERP differ-

ences elicited by word frequency and cognate status in highly

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proficient Spanish--Catalan speakers naming pictures in L1

(Experiment 1) and highly proficient Catalan--Spanish speakers

naming pictures in L2 (Experiment 2). We hypothesized that

the point in time where the frequency and cognate effects

induced significant ERP differences (i.e., the point in time

where the ERPs start to diverge) would provide new insights

regarding the onset of lexical access. We referred to Indefrey

and Levelt’s (2004) study to predict the time windows in which

the effects of word frequency and cognate status should be

expected. In the case of word frequency, we predicted to find

an early lexical modulation between 150 and 200 ms. For the

cognate effect, based on the ERP study of Christoffels et al.

(2007), we predicted to find a slightly later modulation (after

275 ms).

Note that our predictions are solely based on temporal

information and not bound to the modulation of specific ERP

components. The advantage of such a design is that it will

strongly simplify the interpretation of results. It is noteworthy

that not many studies have involved the recording of ERPs

during overt naming (e.g., Christoffels et al. 2007; Ganushchak

and Schiller 2008; Koester and Schiller 2008; Verhoef et al.

2009). However, these studies have investigated bilingual

language control, error monitoring and morphological priming

in picture--word interference, respectively; therefore the

patterns of brain activity elicited in the current study, that is,

in the context of simple picture naming, maybe quite different.

Experiment 1: Highly Proficient Spanish--Catalan Bilinguals NamingPictures in L1

Method

Participants

Eighteen participants took part in the experiment. All were

highly proficient early Spanish--Catalan bilinguals (see Appen-

dix A) exposed almost exclusively to Spanish during their first

3--4 years of life and reported to have a preference or

dominance for that language. Participants were students at

the University of Barcelona (ages 18--25) and received course

credits or monetary compensation for their participation. All

were right-handed, had normal or corrected-to-normal vision

and did not suffer from any neurological or motor problems.

Two of the participants had to be removed from the analyses:

one due to an unacceptable number of EEG artefacts, and

another due to technical problems during EEG recording.

Statistical analyses are thus based on 16 individual data sets.

Materials

Sixty-four line-drawings of familiar objects corresponding to

Spanish words were selected, spanning a wide range of semantic

categories (e.g., body parts, buildings, animals, furniture; see

Fig. 1). Two independent variables were manipulated orthogo-

nally: cognate status and lexical frequency of the picture names.

This design therefore entailed 4 experimental conditions involv-

ing 16 pictures each (see Appendix B). Pictures with high-

frequency names were at least 10 times more frequent than

pictures with low-frequency names (mean lemma frequency

[LEXESP; Sebastian-Galles et al. 2000]: low frequency: 3.9; high

frequency: 52). The mean lexical frequency of the picture

names in the cognate and noncognate sets were similar (non-

cognates: 27.8; cognates: 32.2). The cognate words shared on

average 4 phonological segments with their translation equiv-

alents (range = 2--8). Almost all cognates (27 out of 32) shared at

least the whole first syllable with their corresponding trans-

lations, and all of them shared at least the first phoneme. There

was no obvious phonological or orthographic overlap between

noncognates. None shared their first phoneme and only 7 out of

32 shared the first vowel appearing in a word. Words in the 4

conditions were also matched for length (mean syllable length:

low-frequency noncognates: 2.7; low-frequency cognates: 2.6;

high-frequency noncognates: 2.4; high-frequency cognates: 2.2).

Physical variance within each of the 4 stimulus sets was

evaluated using interstimulus pixel-wise correlations (Thierry

et al. 2007), and no difference was found between experimental

conditions (Fig. 1). In addition, 40 students who did not par-

ticipate in the study rated the complexity of the stimuli using

a Spanish adaptation of the methods described in Snodgrass and

Vanderwant’s (1980) study. We found no differences between

any of the 4 stimulus sets taken 2-by-2 (P > 0.2). Finally, all items

used in the experiment were rated on 3 conceptual variables

(familiarity, typicality, and imageability) by 120 students not

tested in the ERP experiment, using a Spanish adaptation of the

Snodgrass and Vanderwart (1980) procedure. Low- and high-

frequency items only differed significantly (P < 0.001) for the

familiarity ratings but not on typicality or imageability (P > 0.7).

Critically, there were no differences between cognates and

noncognates for any of the 3 conceptual properties (P > 0.5).

Finally, to increase experimental power, each picture was

presented once in each of 6 separate blocks (which makes 64

stimuli per block) with order of the presentation within blocks

randomized for each participant.

Procedure

Participants were tested individually in a soundproof room.

Instructions were administered in Spanish. Participants were

Figure 1. Exemplars of picture stimuli used in the 4 experimental conditions andtheir names in Spanish and Catalan. The images on the right hand side and thebottom depict the standard deviation of each individual picture in a series from themean image in that series. Black shows no difference with the mean, white showsmaximal difference from the mean. There is no differential pattern emerging fromthese standard deviation images, which indicates no systematic bias in interstimulusvariability between experimental conditions.

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asked to name each picture presented in Spanish as fast and as

accurately as possible. Before the experiment started, partic-

ipants were familiarized with the pictures and they were given

feedback (correct picture name) when they made a non

response or an incorrect response (23%). Each experimental

trial had the following structure: 1) a blank interval of 1000 ms

was shown at the centre of a computer screen; 2) a picture was

displayed at the centre of the screen until a response was given

or for a maximum of 1500 ms; 3) a blank intertrial interval of

1000 ms. An asterisk was presented for 500 ms before the first

trial and after the last trial to signal the beginning and end of

each block. Blocks were separated by a 30-s pause. Response

latencies were measured from the onset of the stimulus by

means of a voice key. Stimulus presentation was controlled by

an adaptation of the EXPE Program (Pallier et al. 1997). The

entire experimental session lasted approximately 25 min. At

the end of the experimental session, participants were asked to

fill in a questionnaire regarding language use and proficiency in

their 2 languages.

EEG Procedure

The EEG was continuously recorded from 31 scalp locations

(Fp1, Fpz, Fp2, F3, Fz, F4, F7, F8, FC1, FC2, FC5, FC6, C3, Cz, C4,

CP1, CP2, CP5, CP6, P3 , Pz, P4, PO1, PO2, T3, T4, T5, T6, O1,

Oz, O2) using tin electrodes embedded in an elastic cap. Five

additional electrodes were placed on the participants’ head.

Two bipolar electrodes were placed next to and beneath the

left eye (EOGH and EOGV) to register eye movements; 2 other

electrodes were placed on the participants’ right and left

mastoid (A1 and A2) for on-line referencing and a final

electrode was placed on the participants’ nose as off-line

reference channel. The EEG was continuously recorded and

digitized at 250 Hz. Impedances were reduced to 3 kOhms or

less prior to the beginning of recording. Before segmentation,

the EEG was processed through a low-pass filter with a cut-off

frequency of 20 Hz and a high-pass filter of 0.03 Hz. The EEG

was then segmented into 750-ms-long epochs starting 200 ms

prior to stimulus onset. We chose to segment the EEG only up

to 550 ms after stimulus onset to avoid speech contamination

(e.g., Wohlert 1993; Masaki et al. 2001). Just as in Christoffels

et al. (2007), Verhoef et al. (2009), and Koester and Schiller

(2008) we assumed that analyzing the ERPs before the actual

response would result in artifact-free ERPs. In contrast to those

studies, however, we chose to segment in such a way that 1)

fast responses could not induce motor artifacts and 2) latency

jitter by strong EMG activity could be avoided in the averaging.

For the naming latencies, the fastest average response was

650 ms. We segmented the EEG up to 550 ms (100 ms less than

the fastest average response, to ensure that we could include as

many epochs as possible) and removed prior to averaging all

epochs where the response was faster than 550 ms. The

segmented EEG underwent Gratton and Coles (1989) ocular

correction and artifact rejection where trials with an amplitude

voltage over 100 lV or a change in amplitude between adjacent

samples of more than 200 lV were dismissed. Also trials where

participants’ responses were incorrect or absent and trials

containing other motor artifacts were rejected from the dataset

before averaging. The 750-ms epochs were then averaged in

reference to the 200-ms prestimulus baseline. In total, ERP

analyses were based on an average of 162 segments (SD = 21)

per condition taken 2-by-2 (e.g., low-frequency noncognates +low-frequency cognates for the low-frequency condition) per

subject (low frequency: 164, high frequency: 160, noncognate:

164, cognate: 160).

Analyses

Error Analysis

Four types of responses were scored as errors: 1) production of

incorrect names; 2) verbal disfluencies (stuttering, utterance

repairs, production of nonverbal sounds that triggered the

voice key); 3) recording failures; 4) errors in which the

bilingual participants named the picture in Catalan. This gave

a total of 1.2% erroneous responses. Furthermore, outliers (i.e.,

responses exceeding 3 standard deviations from the partic-

ipant’s mean, 2.7%) and responses faster than 550 ms (10.6%)

were excluded from all analyses. For the ERP analysis another

4.7% of the data were excluded due to artefacts. Given the very

low amount of erroneous responses (1.2%) we decided not to

run a statistical error analysis.

Behavioral Analyses

Separate subject (F1) and item (F2) analyses were carried out

examining 3 independent variables: Cognate Status (cognates

vs. noncognates), Frequency Status (low frequency vs. high

frequency) and Block (6 repetitions).

ERP Analysis

The main goal was to identify the latency at which the ERPs of

the 2 contrasts of interest (low-frequency vs. high-frequency

ERPs and cognate vs. noncognate ERPs) started to diverge

significantly from one another. We adopted a method sug-

gested by Guthrie and Buchwald (1991; see also Thierry et al.

1998, 2003). ERPs were compared between conditions at each

electrode by running 2-tailed paired t-tests at every sampling

point (4 ms) starting from target presentation (0 ms) until at

least a sequence of 12 consecutive t-test samples exceeded the

0.05 significance level. We also estimated the splitting point,

that is, the point in time where ERPs started to diverge in each

individual subject, in order to perform a correlation analysis

between this splitting point and the mean naming latencies of

each subject.

Second, 3 time window analyses were conducted to explore

possible interactions between frequency and cognate status,

and to explore the effect of repetition in the ERPs: 1) a 2 3 2 3

9 repeated measures ANOVA was conducted with Frequency

Status (low frequency vs. high frequency), Cognate Status

(noncognate vs. cognate), and Electrode (9 electrode clusters;

see below) as independent variables; 2) a 2 3 6 3 9 ANOVA was

conducted with Frequency Status, Block (6 blocks) and

Electrode as independent variables; 3) a 2 3 6 3 9 ANOVA

was conducted with Cognate Status, Block, and Electrode as

independent variables. Five time windows were selected, based

upon visual inspection of the Grand Averages: 1) 0--80 ms, 2)

80--160 ms, 3) 160--240 ms (P2), 4) 240--320 ms (N3), and 5)

320--420 ms (P3). Note that P2, N3 and P3 are used as

descriptive labels here. Electrodes were clustered in 9 groups

as follows: Left frontal (Lfr): F7,F3,FC5; Fronto-central (Fc):

Fz,FC1,FC2,Cz; Right frontal (Rfr): F8,F4,FC6; Left Central (Lc):

T3,C3,CP5; Centro-Parietal (Cpar): CP1,CP2,Pz; Right central

(Rc): T4,C4,CP6; Left Parietal (Lpar): T5,P3,PO1; Occipital (Oc):

O1,Oz,O2; Right Parietal (Rpar): T6,P4,PO2).

Thirdly, because the method presented here is new for

studying speech production, correlation analyses were

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performed to help us understand the relationship between the

different phases of information processing indexed by ERPs and

the relationship of these phases with behavioral results. On the

one hand, correlation analyses were performed on the in-

dividual splitting point in the ERPs with the individual mean

naming latencies for the high frequency and cognate condition,

because these are the measures likely to show whether or not

the spitting point indexes the onset of lexical access. On the

other hand, correlation analyses were performed on the peak

latencies of the P2, N3, and P3. Peak latencies were measured at

the electrode of maximum amplitude for each component in

each subject (Picton et al. 2000). Finally, a correlation analysis

was performed on individual differences in naming latencies

(the frequency effect and the cognate effect in each subject)

and the individual mean amplitude differences for each ERP

processing phase in each subject. All correlation analyses were

conducted making use of the same electrode clusters as for the

time window analyses.

Behavioral Results

In the analysis of the naming latencies the main effects of Block

(F1(5,75) = 8.9, MSE = 4867.9, P < 0.001; F2(5,300) = 14.7,

MSE = 2139.9, P < 0.001) and Frequency Status (F1(1,15) =47.9, MSE = 3564.1, P < 0.001; F2(1,60) = 8.5, MSE = 19,890.2,

P = 0.005) were significant, and a marginally significant effect of

Cognate Status was found (F1(1,15) = 42.1, MSE = 1603.9, P <

0.001; F2(1,60) = 3.7, MSE = 19,890.2, P = 0.060). Participants

named pictures with high-frequency names faster than pictures

with low-frequency names, and pictures with cognate names

faster than pictures with noncognate names (see Table 1). The

interaction between Frequency Status and Cognate Status was

significant only in the analysis by subjects (F1(1,15) = 8.0, MSE =2484.8, P = 0.013; F2(1,60) = 1.4, MSE = 19,890.2, P = 0.250).

That is, the cognate effect was larger for high-frequency words

(41 ms) than for low-frequency words (12 ms). Finally, there

was also a significant interaction between Cognate Status and

Block in the subject analysis (F1(5,75) = 3.6, MSE = 1088.5, P =0.014; F2 < 1). However as can be seen in Figure 2 this

interaction was mostly driven by the much smaller cognate

effect in the second block compared with the other blocks.

None of the other interactions were significant (P > 0.1; see

Fig. 2).

ERP Results

Onset Latency Analyses

ERPs displayed a typical P1--N1--P2 peak sequence classically

observed for visual stimulus presentation. t-Tests at each

sampling rate indicated that high-frequency ERPs started to

diverge significantly from low-frequency ERPs 172 ms after

picture onset (see Figs 3a and 4a). As can be seen in Figure 4a,

the distribution of electrodes showing a significant effect at

this time point was particularly left-lateralized (however 4 ms

later, almost all electrodes showed significant differences). The

averaged splitting point computed from individual splitting

point estimates was 167 ms, that is, almost within one time

sampling unit of the splitting time measured in the grand-

averages. Cognate ERPs started to diverge significantly from

noncognate ERPs 200 ms after picture onset (see Figs 3b and

4b). The distribution of electrodes displaying significant differ-

ences at this time point was more right-lateralized (but also

here at the next time point practically all electrodes showed

significant differences). Again the grand-average splitting time

of 200 ms closely resembled the averaged individual splitting

point (192 ms). The difference in onset between the splitting

point of the frequency effect and the splitting point of the

cognate effect was significant (measured by individual splitting

points; P = 0.05).

Time Window Analyses

Early time windows (0--80 ms and 80--160 ms). The only

effect found for all 3 ANOVAs conducted in the early time

windows was a small trend for Block between 80 and 160 ms

(F(5,75) = 2.2, MSE = 20,943.4, P = 0.098). In comparison to the

first repetition, all subsequent repetition ERPs seemed to be

more positive going. However, independent ANOVAs of the

possible contrasts with correction for multiple comparisons

showed no significant effects (P > 0.150). None of the other

main effects or interactions were significant (P > 0.175).

Late time windows (160--240 ms (P2), 240--320 ms (N3),

and 320--420 ms (P3). In all 3 time windows significant main

effects were present for Frequency Status (P2: F(1,15) = 8.9,

MSE = 13.6, P = 0.009; N3: F(1,15) = 33.1, MSE = 31.1, P <

0.001; P3: F(1,15) = 39.1, MSE = 18.8, P < 0.001) and Cognate

Status (P2: F(1,15) = 10.6, MSE = 12.1, P = 0.005; N3: F(1,15) =49.9, MSE = 25, P < 0.001; P3: F(1,15) = 28.1, MSE = 22.4, P <

0.001). ERPs in the high-frequency condition were signifi-

cantly more negative than those elicited in the low-frequency

condition and ERPs in the cognate condition were signifi-

cantly more negative than those elicited in the noncognate

condition. The distribution of this effect emerged at posterior

Table 1Mean naming latencies (ms) in the 4 experimental conditions for Experiment 1 and Experiment 2

Low-frequencynoncognates(ms)

Low-frequencycognates(ms)

High-frequencynoncognates(ms)

High-frequencycognates(ms)

Experiment 1(naming in L1)

730 718 702 661

Experiment 2(naming in L2)

764 742 737 694

620

660

700

740

780

820

1 2 3 4 5 6Blocks

Nam

ing

Late

ncie

s (m

s)

LFNC

LFC

HFNC

HFC

Figure 2. Mean naming latencies in Experiment 1 (LFNC 5 low-frequencynoncognate; LFC 5 low-frequency cognate; HFNC 5 high-frequency noncognate;HFC 5 high-frequency cognate) over repetitions.

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sites of the scalp, but expanded rapidly over large parts of the

entire scalp (see Fig. 5). The interaction between Frequency

Status and Cognate Status was significant, but only for the 2

latest time windows (P2: F < 1; N3: F(1,15) = 6.9, MSE = 14, P =0.019; P3: F(1,15) = 16.6, MSE = 9.4, P = 0.001). Starting around

240-ms poststimulus presentation, amplitude differences

between cognate and noncognate ERPs were larger in the

high than in the low-frequency condition. There was neither

a main effect of Block nor interactions of Block with

Frequency Status and/or Cognate Status in any of the 3 time

windows (P > 0.45).

Correlation Analyses

Pearsons product--moment correlation analyses showed no

significant correlation between the individual splitting point

of the low-frequency versus high-frequency ERPs with the

individual mean naming latencies of the high-frequency

condition, and no significant correlation between the in-

dividual splitting point of the noncognate versus cognate ERPs

with the individual mean naming latencies of the cognate

condition (P > 0.6). The correlations between the individual

peak latencies of the P2, N3, and P3 were not significant

either (P > 0.2).

Fz F4

FC1 FC2

CzC3 C4

CP1 CP2

PzP3 P4

PO2PO1

O2OzO1

-5 [µV]

200 400 550

P2

P3

N3

High Frequency Low Frequency

F4Fz

FC2

Cz

CP2

Pz

PO2

O2Oz

Cognates Non-Cognates

(a)

(b)

C4

P4

-5 [µV]

200 400 550

F3

FC1

C3

CP1

P3

PO1

O1

F3

Figure 3. (a) Low-frequency ERPs compared with high-frequency ERPs in Experiment 1 at anterior, central, and posterior scalp locations. Low-frequency ERPs are representedby a dotted line and high-frequency ERPs by a full line. Negativity is plotted upwards. (b) Noncognate ERPs compared with cognate ERPs of Experiment 1 at anterior, central, andposterior scalp locations. Noncognate ERPs are represented by a dotted line and Cognate ERPs by a full line. Negativity is plotted upwards.

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Significant positive Pearson product--moment correlations

were found in the P2 range between the difference in mean

amplitude between high and low-frequency ERPs and the high-/

low-frequency naming latency difference over left-parietal (R =0.531, P = 0.034) and right parietal electrodes (R = 0.511, P =0.043), and a trend towards a positive correlation for the P2 at

the right-central electrodes (R = 0.455, P = 0.077): The larger the

difference in naming latencies between high- and low-frequency

words, the larger the difference in P2 mean amplitude between

high- and low-frequency conditions.

In the N3 range, only a small trend was found in the same

direction over left (R = 0.477, P = 0.082) and right parietal (R =0.429, P = 0.098) electrode clusters. Finally, in the P3 range,

there were no significant correlations between the frequency

effect in the naming latencies and the mean amplitude

differences in the ERPs (P > 0.250).

Analyses of the cognate effect revealed a remarkably similar

pattern of correlations: Trends towards positive correlations

were found for the cognate effect in the P2 range over left

parietal (R = 0.465, P = 0.070) and right parietal (R = 0.436, P =0.091) electrode clusters. The trend for correlation found in

the P2 range disappeared in the N3 (P > 0.190) and P3 (P >

0.475) ranges. Both the frequency effect and the cognate effect

seemed to relate mostly to the early P2.

Discussion

As expected pictures with high-frequency names yielded faster

naming latencies than those with low-frequency names, and

cognate picture names were produced faster as compared

with noncognate picture names. Interestingly, high-frequency

ERPs started to diverge significantly from low-frequency ERPs

172-ms post-target presentation, with high-frequency picture

names eliciting greater negativities than low-frequency names.

Similar results were found for the cognates: cognate ERPs

started to diverge significantly from the noncognate ERPs

200-ms postpicture presentation with more negativity in the

cognate than in the noncognate condition. For both the fre-

quency and the cognate manipulations, these differences

remained from the moment of the split until the end of the

epoch. The absence of peak latency correlations between the

components that were significantly modulated (P2, N3, P3)

suggests that these components have different functional

underpinnings and that the early differences in the P2 range

are not merely the consequence of latency jitter caused by

stronger variations registered at a later time in each epoch.

Finally, it was shown that the ERP modulations for the fre-

quency and cognate effects were not influenced by repeating

the same stimuli in different blocks.

The time estimate of 172 ms found for the frequency effect is

consistent with the time estimate of 150--175 ms for lexical

access proposed by Indefrey and Levelt (2004). Based on these

authors’ time estimates, both the frequency and the cognate

effect seem to have an early, lexical influence during speech

production. Recall that we used cognate status because it has no

obvious relationship with conceptual variables (see stimulus

ratings) as control for possible conceptual confounds of the

frequency effect. In fact, when plotting cognate and frequency

ERPs together, ERP morphology was remarkably comparable

(see Fig. 6a). Given this similarity and the fact that the cognate

manipulation was not confounded by conceptual variables, we

may interpret the time of split as a good approximation of lexical

access rather than as a difference driven by conceptual factors.

We also need to consider the possibility that the frequency

effect on ERP amplitude may have its origin at a phonological

rather than a lexical level (e.g., Jescheniak and Levelt 1994).

This, however, seems unlikely given that the first significant

ERP differences for both high and low-frequency picture names

and cognate and noncognate picture names occur very early.

Unless one assumes that, once an object is sufficiently

recognized to initiate language related processing (around

150 ms after picture presentation; e.g., Thorpe et al. 1996;

Schmitt et al. 2000), phonological retrieval unfolds in parallel

with lexico-semantic and syntactic retrieval of a word, a pho-

nological account for our findings seems very implausible.

Because the literature does not support the idea that

Figure 4. (a) Low-frequency ERPs compared with high-frequency ERPs inExperiment 1 at PO2 and topographic distribution of electrodes showing a significanteffect at 172 ms after picture presentation (gray area). (b) Noncognate ERPscompared with cognate ERPs in Experiment 1 at PO2 and topographic distribution ofelectrodes showing a significant effect at 200 ms after picture presentation (grayarea).

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phonological and lexical processing proceed in parallel (e.g.,

Salmelin et al. 1994; Van Turennout et al. 1997, 1998; Indefrey

and Levelt 2004), we can conclude that the results here are

indexing lexical access rather than phonological retrieval. Even

more, the present findings do not only show that the first

differences in the ERPs start to emerge at the early P2, but they

also show that the most reliable correlations between ERP

mean amplitude differences and naming latency differences for

the contrasts of interest were present at this component. This

suggests that both these phenomena have an influence during

lexical processing and also that they affect speech production

most strongly at an early point in time, that is, between 160 and

240 ms after picture presentation. In addition, the fact that no

correlations were present between the individual splitting

points in the ERPs for the contrasts at interest and the

individual naming latencies of the fastest conditions of these

contrasts (high-frequency and cognate conditions), suggests

that the time estimate derived from the point of divergence is

indeed indicative for the start (or at least a very early stage) of

lexical access as opposed to its termination. Before considering

the theoretical implications of these findings further, the

robustness and reliability of these results were consolidated by

running a replication experiment. We decided to test Catalan--

Spanish bilinguals performing the same task in their L2

(Spanish) to test whether the timing of the differences would

map onto those found in bilinguals doing the task in their L1. If

the rationale used in the previous experiment is correct we

should be able to replicate these results, regardless of whether

participants name the pictures in their L1 or their L2.

Experiment 2: Catalan--Spanish High Proficient Bilinguals Naming in L2

Method

Participants

Seventeen participants took part in the experiment. All were

highly proficient early Catalan--Spanish bilinguals (a description

of both languages and relative use is presented in Appendix A)

exposed almost exclusively to Catalan during their first 4--5

years of life (see Appendix A) and students at the University of

Barcelona (age range 18--25). Participants received course

credits or monetary compensation for their participation. All

were right-handed, had normal or corrected-to-normal vision

and did not suffer from any neurological or motor problems.

One of the participants had to be removed from the analyses

due to an unacceptably high number of artefacts, leaving 16

participants in the statistical analyses reported below.

Stimuli and Procedures (including EEG acquisition) were

identical to those in Experiment 1.

Analyses

In general, except when specified, all analyses were identical to

those conducted in Experiment 1.

Error Analyses

There were 1.1% erroneous responses, 4.4% outliers, and 3.9%

fast responses which were excluded from the behavioral and

ERP analyses. In addition another 5.3% of the trials were

excluded from the ERP analyses due to artefacts. We decided

not to run a statistical error analysis due to the very low error

rate.

Behavioral Analyses

The behavioral analyses were identical to Experiment 1.

ERP Analyses

The ERP analyses were in all aspects identical to those

described in Experiment 1, except that the time windows

were slightly shifted: 1) 0--80 ms, 2) 80--180 ms, 3) 180--260 ms

(P2), 4) 260--350 ms (N3), and 5) 350--450 ms (P3). In total, ERP

analyses were based on an average of 172 segments (SD = 21)

per condition per subject (low frequency: 173, high frequency:

171, noncognate: 172, cognate: 172).

Figure 5. Spline interpolated grand mean topographies for the differences waves of the frequency (left) and cognate effects (right) at the P2 (split up in 2 time windows of40 ms) in Experiment 1 (upper part; naming in L1) and Experiment 2 (lower part; naming in L2).

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Behavioral Results

Both in the subject and item analyses, significant main effects of

Frequency (F1(1,15) = 45.4, MSE = 2934.7, P < 0.001; F2(1,60) =9.9, MSE = 15014.7, P = 0.003) and Cognate Status (F1(1,15) =33.7, MSE = 2986.2, P < 0.001; F2(1,60) = 7.8, MSE = 15014.7,

P = 0.007) were found. These results replicated those of

Experiment 1 and indicated the presence of frequency and

cognate effects in the naming latencies (see Table 1). The main

effect of Block only reached significance in the item analysis

(F1(5,75) = 2.1, MSE = 10,589.6, P = 0.151; F2(5,300) = 7.3,

MSE = 2397.6, P < 0.001). As in Experiment 1, a significant

interaction between Frequency and Cognate Status was

present for the subject analysis (F1(1,15) = 11.1, MSE = 956.9,

P = 0.005; F2 < 1). The cognate effect was larger for high-

frequency words (43 ms) compared with low-frequency words

(22 ms). Finally, there was a significant interaction between

Frequency and Block in the analysis by subjects (F1(5,75) = 4.9,

MSE = 932.3, P = 0.003; F2(1,60) = 1.6, MSE = 2397.6, P = 0.158)

and a trend toward an interaction between Cognate Status and

Block (F1(5,75) = 2.2, MSE = 851.7, P = 0.081; F2 < 1) only in

the subject analysis. As in Experiment 1 these interactions with

Block did not reveal a stable pattern, but rather randomly

varying sizes of the frequency and cognate effects from block

to block (see Fig. 7). No other interaction reached significance

(P > 0.5).

ERP Results

Onset Latency Analyses

As in Experiment 1, ERPs displayed a typical P1-N1-P2 peak

sequence (see Fig. 8). t-Tests at each sampling rate indicated

that high-frequency ERPs started to diverge significantly from

low-frequency ERPs 184 ms after picture onset (see Figs 8a

and 9a) over anterior, central and posterior midline electrodes

5

Cz-5

Time (ms)

Cz

5

-5

Time (ms)4002000-100 4002000-100

4002000-1004002000-100

Naming in L1 (Experiment 1)

Naming in L2 (Experiment 2)

(b)

(a)

Experiment 1

High FrequencyLow Frequency

Experiment 2

CognateNon-cognate

High Frequency Experiment 1Low Frequency Experiment 1

High Frequency Experiment 2Low Frequency Experiment 2

Cognate Experiment 1Non-Cognate Experiment 1

Cognate Experiment 2Non-Cognate Experiment 2

Ampl

itude

(µV)

Cz

5

-5

Time (ms)

Ampl

itude

(µV)

Cz-5

Time (ms)

5

Cz-5

400Time (ms)

2000-100

5

Figure 6. (a) Low-frequency and high-frequency ERPs compared with noncognate and cognate ERPs at Cz in Experiment 1 (right) and Experiment 2 (left). The frequency ERPsare represented by a full grey and black line. The cognate ERPs are represented by a dotted grey and black line. Negativity is plotted upwards. (b) Between experimentscomparison of the low- and high-frequency ERPs (left), noncognate and cognate ERPs (right), and overall naming in L1 and naming in L2 ERPs (under). Negativity is plottedupwards.

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(at the next time point almost all electrodes showed significant

effects). This time estimate was close to the averaged individual

splitting point (177 ms). The cognate ERPs started to diverge

significantly from noncognate ERPs 184 ms after picture onset

(see Figs 8b and 9b). Furthermore, the time estimate derived

from the grand-averages splitting point overlapped completely

with the averaged individual splitting latencies (184 ms). The

distribution of electrodes showing significant effects at this

time point was widely spread over the scalp (see Fig. 9b). No

significant difference was present between the individual

splitting latency of the frequency effect and that of the cognate

effect (P = 0.4).

Time Window Analyses

Early time windows (0 -- 80 ms and 80 -- 180 ms). None of

the ANOVAs conducted showed significant effects in these

time windows (P > 0.350).

Late time windows (180--260 ms (P2), 260-- 350 ms (N3),

and 350--450 ms (P3). In all 3 time windows significant main

effects were found for Frequency (P2: F(1,15) = 8.9, MSE = 13.6,

P = 0.009; N3: F(1,15) = 74.6, MSE = 23.3, P < 0.001; P3:

F(1,15) = 33.3, MSE = 39.2, P < 0.001) and Cognate Status (P2:

F(1,15) = 10.6, MSE = 12.1, P = 0.005; N3: F(1,15) = 58.3, MSE =22.3, P < 0.001; P3: F(1,15) = 20.6, MSE = 28.5, P < 0.001). ERPs

in the high-frequency condition were significantly more

negative going compared with those elicited by the low-

frequency condition and ERPs in the cognate condition were

significantly more negative going than those in the noncognate

condition. Both effects were widely distributed over the scalp,

reaching their maximum at posterior and right-frontal sites

(see Fig. 5). The interaction between Frequency and Cognate

Status was significant, but only for the 2 later time windows

(P2: F < 1; N3: F(1,15) = 18.8, MSE = 14.1, P = 0.001; P3:

F(1,15) = 20.9, MSE = 11.5, P < 0.001). It is noteworthy that this

pattern of results is identical to that found in Experiment 1.

Finally, as in Experiment 1, in none of the 3 time windows

there was a significant effect of Block (P > 0.250), or

interactions between Block and Frequency (P > 0.150) or

Cognate Status (F < 1).

Correlation Analyses

As in Experiment 1, no significant correlations were found

between the individual splitting point of the frequency and

cognate contrasts in the ERPs with the individual mean naming

latencies of those contrasts in the behavioral data (P > 0.4), nor

did we find significant correlations between the individual peak

latencies of the P2, N3, and P3 (P > 0.36).

Significant positive Pearson product--moment correlations

were found between P2 mean amplitude difference between

high and low frequency and difference in naming latencies

between the high- and low-frequency condition at fronto-

central (R = 0.512, P = 0.042) and left central electrodes (R =0.492, P = 0.053). There was also a trend towards a positive

correlation at centro-parietal electrodes (R = 0.447, P = 0.082).

In the N3 range marginally significant positive correlations

between the frequency effect in the naming latencies and the

frequency effect in the ERPs were present at left parietal (R =0.492, P = 0.053) and left central electrodes (R = 0.483, P =0.058). Small trends toward positive correlations in the same

direction were present at centro-parietal (R = 0.465, P = 0.070)

and right central electrode clusters (R = 0.427, P = 0.099).

Finally, for the P3, only a small trend towards a positive

correlation was present at left parietal electrode sites (R =0.428, P = 0.098).

For the cognate contrast a similar pattern of correlations was

found: There were significant correlations between the

difference in P2 mean amplitude between cognate and

noncognate conditions and the difference in naming latencies

between cognate and noncognate conditions at left frontal (R =0.535, P = 0.033) and left central electrode clusters (R = 0.502,

P = 0.047). A trend towards a positive correlation was present

at fronto-central electrodes (R = 0.449, P = 0.081). As in

Experiment 1 these correlations disappeared in the N3 (P >

0.190) and the P3 (P > 0.560) range, respectively.

Comparison with Experiment 1

Differences in ERP splitting point latencies and RTs between

experiments (i.e., between the 2 participant groups) were not

significant (all P > 0.1). However, we did observe a marginally

significant main effect in the P2 range (and also for subsequent

peaks) between Groups (naming in L1 vs. naming in L2;

F(1,30) = 3.4, MSE = 77.1, P = 0.060; see Fig. 6b) and,

importantly, a marginal significant interaction between Group

and Frequency (F(1,30) = 3.8, MSE = 7.2, P = 0.062; see Fig. 6b).

The frequency effect showed a larger amplitude difference at

the P2 in Experiment 2 (naming in L2; difference: 1.9 lV)compared with Experiment 1 (naming in L1; difference: 1.1 lV).None of the other interactions with Group turned out

significant (all P > 0.1).

Discussion

The results of Experiment 2 in a different group of participants

performing the picture naming task in their L2 were overall

highly similar to those obtained in Experiment 1. First, naming

latencies displayed the expected frequency and cognate

effects, and the interaction between frequency and cognate

status was also replicated. (At the moment we do not have an

explanation for this interaction. A similar interaction has been

reported before in behavioural picture naming experiments,

e.g., Ivanova and Costa 2008, but also the reverse interaction;

e.g., Christoffels et al. 2003, and sometimes none; e.g., Costa

et al. 2000. It might be that the presence and direction of this

620

660

700

740

780

820

1 2 3 4 5 6

Blocks

Nam

ing

Late

ncie

s (m

s)

LFNC

LFC

HFNC

HFC

Figure 7. Mean naming latencies in Experiment 2 (LFNC 5 low-frequencynoncognate; LFC 5 low-frequency cognate; HFNC 5 high-frequency noncognate;HFC 5 high-frequency cognate) over repetitions.

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interaction depends on differences of [uncontrolled] stimulus

properties between experiments. Independently, in both

experiments the interaction becomes apparent in the ERPs

only after the splitting point and the P2 [i.e., after 240 ms],

which is beyond the period of interest in this paper.) Second, in

the ERPs, early differences between low and high frequency

ERPs (184 ms) and between cognate and noncognate ERPs

(184 ms) were observed in a similar time window as those

found in experiment 1. Third, the pattern of correlations

resembled that seen in Experiment 1. Indeed, there were no

correlations between the individual splitting points of the

experimental contrasts and the individual naming latencies of

the fastest condition of those contrasts. There were no

correlations between P2, N3, and P3 peak latencies either,

but significant positive correlations between individual fre-

quency and cognate effects in the naming latencies with mean

amplitude differences for both the frequency and cognate

effects in the ERPs. These correlations were strongest in the P2

range and weaker or absent in the N3 and P3 ranges.

Only 2 qualitative differences were found between the 2

experiments:

1) The frequency effect had an earlier onset compared with

the cognate effect in Experiment 1, but this latency difference

was absent in Experiment 2. Faster conceptual processing for

high frequency words due to their higher familiarity (a

conceptual property), which was not present for the cognate

F3 Fz F4

FC1 FC2

C3 Cz C4

CP1 CP2

P3 Pz P4

PO1 PO2

O1 Oz O2

-5 [µV]

P2

P3

N3

High Frequency Low Frequency

F3 Fz F4

FC1 FC2

C3 Cz

CP1 CP2

P3 Pz

PO1 PO2

O1 Oz O2

Cognates Non-Cognates

(a)

(b)

C4

P4

-5 [µV]

200 550400

200 550400

Figure 8. (a) Low-frequency ERPs compared with high-frequency ERPs in Experiment 2 at anterior, central, and posterior scalp locations. Low-frequency ERPs are representedby a dotted line and high frequency ERPs by a full line. Negativity is plotted upwards. (b) Noncognate ERPs compared with cognate ERPs in Experiment 2 at anterior, central, andposterior scalp locations. Noncognate ERPs are represented by a dotted line and Cognate ERPs by a full line. Negativity is plotted upwards.

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contrast, could account for the faster engagement in lexical

processing for those items and consequently for the earlier

splitting latency. However, the similarity in splitting latency for

lexical frequency and cognate status in Experiment 2 invalidates

this account. In addition, given that analyses of splitting latencies

for lexical frequency and cognate status are based on different

sets of stimuli (50% different), it is likely that conceptual

processing has a different duration on average for each picture

set (in that sense the similarity in Experiment 2 is more sur-

prising than the different onset in Experiment 1). Nevertheless,

ERPs had the same morphology in both conditions (see Fig. 6a)

and amplitude differences were found in the time window

previously associated with lexical processing (175--250 ms

according to Indefrey and Levelt 2004).

Second, we encountered more positive P2 amplitudes in

Experiment 2 (naming in L2) as compared with Experiment 1

(naming in L1; see Fig. 6b). This is an important observation in

light of our theoretical claims. (This observation is also of

importance regarding the bilingual naming disadvantage in the

non dominant language; e.g., Indefrey 2006; Ivanova and Costa

2008). However, because this topic does not fall under the

scope of the present study, this result will only be discussed in

light of the theoretical claims made in the present study. These

findings with respect to the bilingual naming disadvantage will

be discussed elsewhere [including more subjects in each group

and adding a within-subjects experiment].) One may argue that

due to the interactivity of the brain, different representational

systems which are interconnected, such as the lexical system

and the semantic (or object—imaginal—representation)

system, may benefit and even share processing activation from

one domain to the other (e.g., Paivio 1986). In such a scenario

our results would still reflect the first influence of lexical

variables during speech production processing (given the

cognates), but not necessarily processing solely at a lexical

level. This being said, the dual-code view cannot, however,

account for the amplitude difference between L1 and L2

naming in the P2 range (see Fig. 6b). Naming in L1 versus

L2 should activate the exact same semantic (object) represen-

tation (e.g., Kroll and Stewart 1994) and a difference between

the 2 can only be explained at the lexical level where the

representational format is distinct (recall that subjects are

early, highly proficient bilinguals using both languages on

a daily basis, and that stimuli were concrete words). Although

the validity of between group comparisons can be disputed

(especially with ERPs), it is difficult to imagine that 2 groups of

participants viewing the exact same images overall would

display between-group differences by chance in the same time

range (~192 ms) and in the same manner as differences

generated by lexical frequency and cognate status manipu-

lations. This is especially true because we also encountered

a (marginally) significant interaction with lexical frequency

between experiments. Such pattern of results is unlikely to

sprout from coincidental between-group differences. A stron-

ger P2 modulation for the frequency effect during L2 naming

compared with L1 naming can only be readily explained by

assuming that these effects occur at the lexical level. In

addition, the fact that for lexical frequency there was

a difference in familiarity (a conceptual property) while this

difference was absent for cognate status, and that both

variables elicited similar ERP effects, also argues against the

possibility that present results merely reflect lexical influences

during conceptual processing.

General Discussion

The main aim of this study was to characterize the time course

of lexical access in speech production using a high-resolution

temporal technique, event-related potentials. Two effects

known to affect picture naming latencies were investigated:

the word frequency and the cognate effects. Participants

showed reliable and robust frequency and cognate effects in

both experiments, replicating previous studies (e.g., Oldfield

and Wingfield 1965; Jescheniak and Levelt 1994; Costa et al.

2000; Navarrete et al. 2006; Almeida et al. 2007; Christoffels

et al. 2007). Crucially, we found early ERP effects of frequency

and cognate status, independently of whether naming was

Figure 9. (a) Low-frequency ERPs compared with high-frequency ERPs inExperiment 2 at PO2 and topographic distribution of electrodes showing a significanteffect at 184 ms after picture presentation (grey area). (b) Noncognate ERPscompared with cognate ERPs in Experiment 1 at PO2 and topographic distribution ofelectrodes showing a significant effect at 184 ms after picture presentation (greyarea).

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performed in L1 or L2. High-frequency ERPs diverged from

low-frequency ERPs at around 180 ms after picture onset

(172 ms in Experiment 1, 184 ms in Experiment 2) coinciding

with the onset of a positive wave (P2), with the high lexical

frequency condition eliciting lower ERP amplitudes than the

low-frequency condition. A similar pattern of results was found

for cognates: pictures with cognate names started to diverge

from those elicited by pictures with noncognate names around

190 ms after stimulus presentation (200 ms in Experiment 1;

184 ms in Experiment 2), with noncognates eliciting greater

amplitudes than cognates.

The Word Frequency Effect as an Index of Lexical Access

The early effect of word frequency in picture naming suggests

that speakers start the lexicalization process very early on after

picture presentation. That is, to the extent that word frequency

affects lexical retrieval, we propose that participants started the

lexicalization processes between 150 and 200 ms after picture

onset. This time estimation is consistent with results from MEG

studies (e.g., Levelt et al. 1998; Maess et al. 2002) and covert

‘‘lexical’’ ERP studies (e.g., Schmitt et al. 2000), and fundamen-

tally agrees with the meta-analysis conducted by Indefrey and

Levelt (2004). Importantly, the early effect of word frequency

during picture naming is unlikely to index the time of retrieval

of target lexical representations given the absence of correlation

between ERP splitting latencies and naming latencies for high-

frequency words. Instead, we propose that this effect coincides

with initial activation and retrieval operations within the lexicon.

That is, the point in time where a lexical representation gets

activated and enters in the competitive process of selection,

with high-frequency items showing a head start over low-

frequency items due to their (permanently) higher activation

levels. The splitting point between ERPs can therefore be seen as

the transition phase between conceptual and lexical selection

processes (see e.g., Thorpe et al. 1996; Hauk et al. 2007; for

object recognition time estimates).

Besides the descriptive chronometric information provided

by our results, the present findings also have implications for

the locus of the frequency effect in speech production. As

mentioned in the introduction, word frequency affects solely

the retrieval of the phonological properties of a word accord-

ing to some authors (e.g., Jescheniak and Levelt 1994;

Jescheniak et al. 2003). Under this assumption, we should

have expected the ERPs in the high- and low-frequency

conditions to start diverging when a word’s phonological code

is supposed to be retrieved, that is, around 275 ms according to

Indefrey and Levelt (2004). The presence of a word frequency

effect at ~180 ms is at odds with this position, and suggests that

word frequency also affects the speed with which lexical items

are retrieved from the lexicon (e.g., Caramazza et al. 2001;

Navarrete et al. 2006; Almeida et al. 2007).

However, finding an early effect of word frequency does not

discard an effect at later processing stages, such as that of

phonological encoding. Indeed, our results show that differ-

ences between low-frequency and high-frequency ERPs are

present in later time windows as well. In fact, the amplitude of

the ERPs for high- and low-frequency words correlated

positively with naming latency differences for time windows

at which lexical (the P2 window) but also phonological

encoding (the N3 window) are supposed to take place.

Therefore, considering hypothetically that different cognitive

processes take place in these different time windows (e.g.,

lexical access -early stage-, phonological access -late stage-), we

can conclude that both processing stages are affected by word

frequency. This view of the ubiquitous effects of word

frequency is consistent with recent hemodynamic evidence

showing that word frequency modulates the activity of brain

areas thought to be involved in the retrieval of lexico-semantic

as well as phonological information (Graves et al. 2007) and

recent studies with brain-damaged patients showing frequency

effects for semantically and phonologically related errors and

errors resulting in nonwords (e.g., Kittridge et al. 2007; Knobel

et al. 2008).

A possible caveat when interpreting the early effect of word

frequency in the ERP data is the potential correlation of this

variable with conceptual variables such as familiarity and

imageability. Indeed, stimuli ratings on conceptual variables

conducted for the pictures used in the present experiments

showed significant familiarity differences between the low-

frequency and high-frequency items. This means that the early

ERP effects for frequency could be driven by these conceptual

differences. However, we also found early cognate effects in

the ERPs, in a rather similar time window to that of the

frequency effects. In fact, when plotted together the electro-

physiological signature of the cognate effect and the frequency

effect are practically identical (see Fig. 6a) and the correlation

patterns between behavioral differences and electrophysiolog-

ical differences are also similar. Given that -unlike word

frequency cognate status is not correlated with conceptual

variables (see also stimuli ratings), the early effect of cognate

status cannot be interpreted as a mere effect of correlated

conceptual variables, but rather some sort of lexical effect.

Thus, if one is willing to interpret the cognate effect at such

early time as revealing lexical processing, it is reasonable to

interpret word frequency effects along the same lines.

On the Origin of the Cognate Effect

As mentioned above, the results of the cognate manipulation are

useful when interpreting the word frequency effects. In

addition, the early effect of cognate status also sheds light on

its origin. Interestingly, such an effect was also descriptively

reported in the study by Christoffels et al. (2007, personal

communication), who found significant differences between

cognate and noncognate ERPs as early as 175 ms after picture

presentation.

The parallel results observed for word frequency and

cognate manipulations suggest that the 2 effects might have

the same origin at the lexical level. Because of the phonological

overlap between a cognate and its translation, every time

a cognate word is heard or uttered, both the target lexical

representation and its translation are strongly activated,

irrespective of the language of utterance. In contrast, when

a noncognate word is produced or heard, the translation word

will probably not be activated that strongly, given the lack of

phonological overlap. Following this rationale, cognate lexical

representations should have a higher frequency than non-

cognate lexical representations, because the former are

activated more often. (Both interactive, e.g., Dell 1986, as

sequential, e.g., Levelt, et al. 1998, models of speech production

can nicely capture this assumption. According to interactive

models, the activated phonological segments of the target word

will send activation back to any word with which they are

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linked. In such scenario, the utterance of a cognate word will

cause its translation to become lexical activated as well, due to

feedback activation from shared phonological segments onto

the lexical level with which these segments are linked. A

similar process will not unfold for noncognate translations,

because they hardly share phonological segments; see Costa

et al. 2005. Sequential models can explain a cognate effect at

the lexical level in a similar manner through comprehension:

every time a cognate is heard [also through perception of one’s

own voice], the similar phonological content will also activate

the nontarget lexical representation, whereas for noncognates

this bottom-up activation via phonology will only result in

activation of the target word.) Consequently, the cognate effect

may reflect a word frequency effect in disguise, with cognate

words behaving as high-frequency words and noncognate

words as low-frequency words (see also Kirsner et al. 1993;

Sanchez-Casas and Garcıa-Albea 2005, for an alternative

explanation of cognate effects at the lexical level). Note that

a similar explanation at the conceptual level cannot account for

the cognate effect because the conceptual representations of

cognates and noncognates are considered to be shared

between L1 and L2 (e.g., Kroll and Stewart 1994).

Independently of the precise nature of the cognate effect,

our results reveal that cognate status has an effect at early

stages of lexical access. However, as in the case of word

frequency, this early effect does not preclude effects of

cognate status at subsequent processing levels. For instance,

Christoffels et al. (2007) reported ERP cognate effects between

275 and 375 ms after target presentation, with enhanced

negative amplitude for cognate relative to noncognate ERPs.

Consistent with this late effect, we found a similar modulation

at the N3. Thus, as reported previously for word frequency,

cognate status appears to affect both lexical processing (P2

range) and phonological encoding (N3 range), and therefore

seems to affect picture naming latencies in a similar way as

word frequency.

Methodological Issues and ERP Components of Interest

So far we have mainly discussed the data in the temporal

domain. Given the novelty of EEG studies using overt picture

naming, it is pertinent to dedicate some words to the ERP

components of interest identified in the experiments. Before

doing so, however, some potential methodological pitfalls need

to be discussed.

In the experiments there were 16 stimuli per condition (32

per experimental contrast), and many repetitions were

needed (6) in order to obtain enough trials per condition.

Stimulus repetition in ERPs can be a substantial source of

modulation (e.g., Bentin and McCarthy 1994; Rugg and Doyle

1994). The repetition effects in this study were however

negligible and, critically, there were no interactions between

repetition and other factors in either experiments, suggesting

that stimulus repetition did not affect the frequency and

cognate effects reported. In particular, the P2 modulation by

frequency and cognate status had the same magnitude in all

experimental blocks. The small magnitude of repetition

effects observed, may be due to 1) the fact that we did not

record the first presentation of the stimuli (familiarization

phase). Therefore we could not measure the ERP differences

between the familiarization phase and the first experimental

presentation, where the strongest ERP repetition effects are

to be expected. Indeed, ERP studies using multiple repetitions

have shown that ERP differences elicited by repetition are

largest for the first repetition and seem to decrease or even

vanish with subsequent repetitions (e.g., Gruber and Muller

2005; Friedman and Cycowicz 2006); 2) the irregularity of the

lag between repeated items (e.g., Henson et al. 2004); 3) the

relatively large average lag between repeated stimuli (e.g.,

Henson et al. 2004).

Another possible confound in the present study is that

different physical stimuli were used in the different experi-

mental conditions. This could result in spurious ERP amplitude

modulations caused by physical stimulus differences rather

than the cognitive manipulation of interest (e.g., Picton et al.

2000). However, because 50% of the stimuli were completely

different between the high-frequency and cognate condition,

finding similar time courses of differences and correlation

patterns by chance is unlikely. In addition, the ERP pattern

correlated with the behavioral results, where naming latency

differences are less likely to sprout from distinct physical

stimuli. Finally, when comparing directly L1 against L2 naming

overall, that is, when comparing ERPs elicited by the exact

same set of pictures in 2 different naming contexts, we find the

same P2 modulation as in experiments 1 and 2 taken separately

(see Fig. 6b).

The ERP components observed here were not systematically

interpreted in a traditional way because this study focused on

divergence latencies and amplitude-naming latency correlations.

Nevertheless, the peaks observed may be related to classical

components described in the literature. For instance, P3 mean

amplitude was more pronounced for low- than for high-

frequency words, which may suggest postlexical reprocessing

of word-related information (e.g., Polich and Donchin 1988;

Hauk and Pulvermuller 2004). However, given the marked

positivity of the P3 component in our dataset, amplitude

modulation could also partially reflect early stages of motor

preparation.

Perhaps the most interesting results were found for the P2

range, given the correlation between frequency and cognate

naming latency and P2 mean amplitude difference between

high- and low-frequency picture names and between cognates

and noncognates. Individuals showing larger frequency and

cognate naming latency effects also showed bigger P2 mean

amplitude differences for the same contrasts. In other words, P2

amplitude appears to reflect the ease of lexical access, with

lower amplitudes associated with easily accessible representa-

tions (high frequency words and cognates) and larger ampli-

tudes associated with less accessible representations. Such

amplitude modulations are consistent with Hebbian theory of

cell assemblies (e.g., Pulvermuller 1999; Hauk and Pulvermuller

2004). Another possible interpretation for the P2 comes from

word recognition experiments manipulating vocabulary class

(e.g., King and Kutas 1998; Brown et al. 1999; Osterhout et al.

2002). These studies reported, in a similar vein as observed in

the present study, reduced P2 amplitudes for closed class (faster

condition) as compared with open class words (slower

condition). Although these studies did not elaborate much on

this finding, it was suggested that the P2 modulation might

reflect attentional differences between nonlexical aspects of the

stimuli such as length (cf., Mangun and Hillyard 1995). For the

present study however, such an account does not seem valid.

Given the results observed for the cognates, as explained

extensively above, the P2 modulations reported here have to be

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related, at least in part, to linguistic processes. It is possible that

these P2 effects are indeed confounded by attention, with rare

stimuli eliciting larger attentional shifts than more common

stimuli (e.g., Luck and Hillyard 1994), but this would not take

away the value of our observation because, in that case, these P2

differences most likely reflect attentional resources needed

during lexical activation. Future research exploring further the

functional characteristics of the P2 component will probably

provide fundamentally new insights regarding lexical access in

speech production.

Conclusion

For the first time, early electrophysiological differences elicited

by manipulation of lexical frequency and cognate status during

overt picture naming were established. Based on the latency of

ERP divergences between conditions, lexical access is esti-

mated to occur at around 180 ms after target presentation.

Aside from this important chronometric contribution, the

present study offers a promising new paradigm using a simple

task and a simple experimental design to study the time course

of speech production. In addition, by identifying an early

electrophysiological correlate of lexical processing, this study

may be a starting point for approaching a variety of

psycholinguistic phenomena in language production from

a whole new perspective.

Funding

Spanish Government grant (PSI2008-01191); Project Consolider-

Ingenio 2010 (CSD 2007-00012); and Spanish Government

(FPU-2007-2011) predoctoral fellowship supported K.S.

Notes

We would like to thank Phillip Holcomb and 2 anonymous reviewers for

their helpful comments to the previous version of this manuscript. We

would also like to thank Elin Runnqvist for her help in revising this

manuscript. Conflict of Interest : None declared.

Address correspondence to Albert Costa, PhD, Dept. de Tecnologia,

ICREA, Universitat de Pompeu Fabra, C/ Tanger, 122-140, 08018

Barcelona, Spain. Email: costalbert@gmail.com.

Appendix A. Language history and the self-assessed proficiency forall participants

Language history and self-assessed proficiency scores of participants.

Mean age and SD are given in years. The onset of L2 acquisition refers

to the mean age (in years) at which participants started learning

Catalan/Spanish. ‘‘Use of L2’’ refers to how long (in years) participants

have been using the L2 regularly. The proficiency scores were obtained

through a questionnaire filled out by the participants after the

experiment. The scores are on a 4 point scale, in which 4 represents

native speaker level; 3, good level; 2, medium level; and 1, poor level of

proficiency. The self-assessed index is the average of the participants’

responses to 4 domains (speech comprehension, speech production,

reading, and writing).

Appendix B: List of stimuli used in the Experiment

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